garage.tf.regressors.gaussian_cnn_regressor_model module

GaussianCNNRegressorModel.

class GaussianCNNRegressorModel(input_shape, output_dim, name='GaussianCNNRegressorModel', **kwargs)[source]

Bases: garage.tf.models.gaussian_cnn_model.GaussianCNNModel

GaussianCNNRegressor based on garage.tf.models.Model class.

This class can be used to perform regression by fitting a Gaussian distribution to the outputs.

Parameters:
  • filter_dims (tuple[int]) – Dimension of the filters. For example, (3, 5) means there are two convolutional layers. The filter for first layer is of dimension (3 x 3) and the second one is of dimension (5 x 5).
  • num_filters (tuple[int]) – Number of filters. For example, (3, 32) means there are two convolutional layers. The filter for the first layer has 3 channels and the second one with 32 channels.
  • strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
  • padding (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
  • output_dim (int) – Output dimension of the model.
  • name (str) – Model name, also the variable scope.
  • hidden_sizes (list[int]) – Output dimension of dense layer(s) for the Convolutional model for mean. For example, (32, 32) means the network consists of two dense layers, each with 32 hidden units.
  • hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
  • hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
  • hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
  • output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
  • output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.
  • output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
  • learn_std (bool) – Is std trainable.
  • init_std (float) – Initial value for std.
  • adaptive_std (bool) – Is std a neural network. If False, it will be a parameter.
  • std_share_network (bool) – Boolean for whether mean and std share the same network.
  • std_filter_dims (tuple[int]) – Dimension of the filters. For example, (3, 5) means there are two convolutional layers. The filter for first layer is of dimension (3 x 3) and the second one is of dimension (5 x 5).
  • std_num_filters (tuple[int]) – Number of filters. For example, (3, 32) means there are two convolutional layers. The filter for the first layer has 3 channels and the second one with 32 channels.
  • std_strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
  • std_padding (str) – The type of padding algorithm to use in std network, either ‘SAME’ or ‘VALID’.
  • std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the Conv for std. For example, (32, 32) means the Conv consists of two hidden layers, each with 32 hidden units.
  • min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues.
  • max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues.
  • std_hidden_nonlinearity – Nonlinearity for each hidden layer in the std network.
  • std_output_nonlinearity (callable) – Activation function for output dense layer in the std network. It should return a tf.Tensor. Set it to None to maintain a linear activation.
  • std_output_w_init (callable) – Initializer function for the weight of output dense layer(s) in the std network.
  • std_parametrization (str) –

    How the std should be parametrized. There are two options: - exp: the logarithm of the std will be stored, and applied a

    exponential transformation
    • softplus: the std will be computed as log(1+exp(x))
  • layer_normalization (bool) – Bool for using layer normalization or not.
network_output_spec()[source]

Network output spec.